Analyzing resilience properties in oscillatory biological systems using parametric model checking

Simulating ATO Mechanism and EGFR Signaling with Fuzzy Logic and Petri Net

BACKGROUND

Interactions of many key proteins or genes in signalling pathway have been studied qualitatively in the literature, but only little quantitative information is available.

OBJECTIVE

Although much has been done to clarify the biochemistry of transcriptional dynamics in signalling pathway, it remains difficult to find out and predict quantitative responses. The aim of this study is to construct a computational model of epidermal growth factor receptor (EGFR) signalling pathway as one of hallmarks of cancer so as to predict quantitative responses.

MATERIAL AND METHODS

In this analytical study, we presented a computational model to investigate EGFR signalling pathway. Interaction of Arsenic trioxide (ATO) with EGFR signalling pathway factors has been elicited by systematic search in data bases, as ATO is one of the mysterious chemotherapy agents that control EGFR expression in cancer. ATO has dichotomous manner in vivo, dependent on its concentration. According to fuzzy rules based upon qualitative knowledge and Petri Net, we can construct a quantitative model to describe ATO mechanism in EGFR signalling pathway.

RESULTS

By Fuzzy Logic models that have the potential to trade with the loss of quantitative information on how different species interact, along with Petri net quantitatively describe the dynamics of EGFR signalling pathway. By this model the dynamic of different factors in EGFR signalling pathway is achieved.

CONCLUSION

The use of Fuzzy Logic and PNs in biological network modelling causes a deeper understanding and comprehensive analysis of the biological networks.

High-speed parameter search of dynamic biological pathways from time-course transcriptomic profiles using high-level Petri net

Dynamic simulation promises a deeper understanding of complex molecular mechanisms of biological pathways. How to determine the reaction kinetic parameters which govern the simulation results is still an open question in the field of systems biology. (1) Background: To execute simulation experiments, it is an essential first step to search effective values of model parameters. The complexity of biological systems and the experimental measurement technology severely limit the acquirement of accurate kinetic parameters. Previously proposed genomic data assimilation (GDA) approach enables users to handle parameter estimation using time-course information. However, it highly depends on successive time points and costs massive computational resource; (2) Methods: To address this problem, we present a new high-speed parameter search method for estimating the kinetic parameters of quantitative biological pathways using time-course transcriptomic profiles. The key idea of our method is to interactively prune the search space by introducing Probabilistic Linear-time Temporal Logic (PLTL) based model checking into GDA. (3) Results and conclusion: We demonstrated the effectiveness of our method by comparing with GDA on Mus musculus transcription circuits modelled by hybrid functional Petri net with extension. As a result, our method works faster and more accurate than GDA for both time-course datasets with dense and sparse observed values.

Circadian Rhythm and Pain: Mathematical Model based on Multiagent Simulation

The circadian rhythm is responsible for the daily variations in metabolism, and circadian rhythm disorders have direct implications for many diseases, such as obesity and mental disorders. The regulation of sleep time is the most common example of the importance of the circadian rhythm for the functioning of the human body. In this sense, this work aims to study a mathematical and computational model based on multiagent simulation that simulates the synchronization and desynchronization of the circadian rhythm in relation to the pain variables. The results from the multiagent simulation of circadian rhythms show that in relation to pain, sleep, especially its biological rhythms, is directly affected by pain. In this way, our mathematical model was able to show that pain causes changes in the circadian rhythm and it can contribute to the medical field analysis.